Malaria is one of the most severe public health problems worldwide with 300 to 500 million cases and about one million deaths reported to date, 90% of which were reported from Sub Saharan African countries like Ethiopia. The main objective of the study was identification of malaria hazard areas by using the Arc GIS in East Gojjam zone. Weighted overlay technique of multi-criteria analysis was used to develop the malaria-hazard map. Temperature, rainfall, altitude, slope, distance from rivers, and soil types were considered as variables to prepare malaria hazard map. The malaria hazard map was classified into four suitability index such as very high suitable, high suitable, moderately suitable, and low suitable. The result shows that around 22% areas is highly suitable for malaria hazard, 27% is high suitable, 26% is moderately suitable and 25 % is low suitable for malaria hazard areas. It is suggested that effective identification and mapping of malaria hazard areas may contribute for the prevention system cost effective, least time taking, easily manageable in controlling the disease.

1. Identifying Malaria Hazard Areas Using GIS and Multi Criteria: The Case Study at East Gojjam Zone, Ethiopia
Identifying Malaria Hazard Areas Using GIS and Multi
Criteria: The Case Study at East Gojjam Zone, Ethiopia
*1Abinet Addis, 2Bewketu Assefa, 3Tehtenaw Kefyalew
1Department of Civil Engineering, Debre Markos University, Debre Markos, Ethiopia
2,3Department of Hydraulics and Water resource Engineering, Debre Markos University, Debre Markos, Ethiopia
Malaria is one of the most severe public health problems worldwide with 300 to 500 million cases
and about one million deaths reported to date, 90% of which were reported from Sub Saharan
African countries like Ethiopia. The main objective of the study was identification of malaria
hazard areas by using the Arc GIS in East Gojjam zone. Weighted overlay technique of multi-
criteria analysis was used to develop the malaria-hazard map. Temperature, rainfall, altitude,
slope, distance from rivers, and soil types were considered as variables to prepare malaria hazard
map. The malaria hazard map was classified into four suitability index such as very high suitable,
high suitable, moderately suitable, and low suitable. The result shows that around 22% areas is
highly suitable for malaria hazard, 27% is high suitable, 26% is moderately suitable and 25 % is
low suitable for malaria hazard areas. It is suggested that effective identification and mapping of
malaria hazard areas may contribute for the prevention system cost effective, least time taking,
easily manageable in controlling the disease.
Key words: East Gojjam Zone, Arc GIS, Malaria.
INTRODUCTION
Malaria is a highly killer disease that affects majority of the
world’s populations especially those people living in sub-
Saharan Africa countries (Francis et al., 2016). It is
estimated that at least 3.3 billion people globally are at risk
of malaria infection. The disease is responsible for over
half a million deaths each year, mostly (90%) in sub
Saharan Africa (Onyango et al., 2016). Ethiopia is a
predominantly malaria prone country as about 75% of the
landscape of the country is favorable for breeding of the
malaria vector (Embet et al., 2016). According to WHO,
an estimated 627, 000 deaths occurred due to malaria in
2012 (WHO, 2012). In many developing nations with
diverse ecological regions, malaria is still a large cause of
human mortality (Francis et al., 2016, Bindu and Janak,
2014)]. Owing to the paucity in epidemiological data and
their spatial origin, the quantification of disease incidence
burdening basic public health planning is a major constrain
especially in developing countries (Bindu and Janak,
2014).
Malaria as a vector borne disease whose transmission and
risk levels depend on environmental factors, therefore, the
distribution of malaria in Ethiopia is largely determined by
altitude as it influence the temperature of the environ
(Betemariam and Yayeh, 2002). One of the for instance,
is temperature, as it affects mosquito development rate
and final survival of the adult mosquitoes. Vegetation
types, population density, poverty levels together with
other development and social economic factors also
greatly the influence malaria risk levels in a given locality
(Francis et al., 2016).
Geographic Information System (GIS) is a novel
technology that has evolved as a front runner in the study
of the epidemiology of Malaria. Geographic Information
Systems are a useful tool to generate interactive malaria
hazard maps allowing the management and analysis of
multiple databases taking into account the geographical
component of the different hazard factors. Multiple criteria
decision analysis approaches are used to deal with the
difficulties that decision-makers encounter in handling
*Corresponding Author: Abinet Addis, Department of
Civil Engineering, Debre Markos University, Debre
Markos, Ethiopia. E-mail: abinet_addis@dmu.edu.et
Co-Authors 2
Email: Bewket.Assefa@yahoo.com;
3
Email: tehtenawK@gmail.com
Research Article
Vol. 6(1), pp. 132-137, February, 2020. © www.premierpublishers.org. ISSN: 2021-6009
International Journal of Geography and Regional Planning

2. Identifying Malaria Hazard Areas Using GIS and Multi Criteria: The Case Study at East Gojjam Zone, Ethiopia
Addis et al. 133
Fig.1: Location Map of the study area
large extents of complex information. The principle of the
method is to divide the decision problems into smaller
understandable parts, to analyze each part separately and
then to integrate the parts in a logical manner (Malczewski,
1997). The purpose of this study is to use ArcGIS tools to
identify the malaria hazard areas in the study area
integrated with multi-criteria decision analysis method,
which consists of the analytical hierarchy process (AHP)
and weighted linear combination (WLC) methods.
MATERIALS AND METHODS
Study Area
East Gojjam Administrative Zone is one of the eleven
Zones of Amhara National Regional State and constitutes
20 Woredas (16 rural woredas, and 4 town administration
Woredas). It is bordered on the south by the Oromia
Region, on the west by Mirab Gojjam, on the north by
Debub Gondar, and on the east by Debub Wollo; the bend
of the Abay River defines the Zone's northern, eastern and
southern boundaries. Its highest point is Mount Choqa
(also known as Mount Birhan). According from UTM
coordinate system, the location of the town is
approximately between 287743m – 449349m East and
1088275m –1242618m North (Figure 1).
Methods
East Gojjam zone was selected to identify malaria hazard
areas. For this Multi Criteria Analysis was used for creating
various layers to be used in GIS domain to produce a
single output map. The weights were developed by
providing a series of pair wise comparisons of relative
importance. Based on experience and likely impact on
surrounding environment different weights were assigned
to all the parameters. Weighted linear combination was
used to produce the suitability of malaria hazard map. As
for the final weighted factor map is a weighted linear
combination of factor maps, an equation (1) as following:
S = Σ wi xi (1)
where, S = suitability, wi = weight of factor i and xi = factor
map i.

3. Identifying Malaria Hazard Areas Using GIS and Multi Criteria: The Case Study at East Gojjam Zone, Ethiopia
Int. J. Geogr. Reg. Plan. 134
Fig. 2: Flow chart of the methodology
Table 1: Data type and their sources
No. Types of
Datasets
Format Sources
1 Surface water
(river/streams)
Vector,
Shape file
Interpretation of ASTER
GDEM 30m image
2 Soil texture Vector,
Shape file
Minister of Water and
Energy, Addis Ababa
3 Slope Raster Interpretation of ASTER
GDEM 30m image
4 Elevation/
altitude
Raster Interpretation of ASTER
GDEM 30m image
5 Temperature Vector,
Shape file
Ethiopia National
Meteorological Service
Agency, Addis Ababa
6 Rainfall Vector,
Shape file
Ethiopia National
Meteorological Service
Agency, Addis Ababa
RESULTS AND DISCUSSION
Factors for Identify Malaria Hazard Areas
Slope
Slope is one of the topographical factors that have a
bearing on the incidence of malaria. It is vital to include
slope factor with other factors to assess for the malaria risk
in community. This is because slope determines the
mosquito’s larva habitat formation (Dilip et al., 2016). If a
particular area that composed of steepest slopes, then
there will be no chance of mosquitoes’ lava habitat
formation, and hence area would have a negative
influence to malaria incidence. If a particular area has a
gentle slope, then mosquito’s’ larva formation is possible,
hence leading to more positive bearing in malaria
incidence.
Fig. 3: Slope map
Table 2: Classification, ranking and weighting of Slope
factor
Factor Weight Class Rank Degree of hazard
Slope
(degree) 0.1
0-2 4 Very High
Suitability
2-5 3 High Suitability
5-10 2 Moderate
Suitability
>10 1 Low Suitability
Altitude
Altitude is one of the prominent factors; that was used to
assess malaria hazard in the study area. The whole idea
into integrating altitude factor for assessing the malaria
hazard or risk is that of temperature difference within coast
areas and mountainous areas of the study area. The
higher terrain or high mountains have low temperature
compared to low land areas that do have high temperature.
Thus, mosquito’s habitats are well suited with low land
areas, where the temperature is relatively higher.
Communities living in highlands are subjected to fewer
malaria incidence compared to communities living in low
land areas where the chance of vector induced infections
is high because most mosquito’s’ habitats are found in low
laying areas. Higher altitude depicts less malaria risk while
low altitude depicts more malaria risk problems based on
high or low temperature (Dilip et al., 2016).

4. Identifying Malaria Hazard Areas Using GIS and Multi Criteria: The Case Study at East Gojjam Zone, Ethiopia
Addis et al. 135
Fig. 4: Altitude map
Table 3: Classification, ranking and weighting of Altitude
factor
Factor Weight Class Rank Degree of hazard
Altitude
(meter)
0.35
840-
1760
4 Very High
Suitability
1760-
2260
3 High Suitability
2260-
2500
2 Moderate
Suitability
2500-
4060
1 Low Suitability
Rainfall
Rainfall plays a significant role in creating favorable for
malaria transmission and also the high source of water is
more comfortable for malaria breeding. When there is a
heavy rainfall, thus mosquito breeding is unfavorable
during the rainy period since excessive rains cause
flushing, thus killing immature stages to complete its life
cycle to be an adult.
Fig. 5: Rainfall map
Table 4: Classification, ranking and weighting of Rainfall
factor
Factor Weight Class Rank Degree of
hazard
Annual
rainfall (mm)
0.15
200-
300
1 Low
Suitability
300-
400
2 Moderate
Suitability
400-
450
3 High
Suitability
450-
500
4 Very High
Suitability
Temperature
The ranges of minimum and maximum temperature greatly
affect the development of the malaria parasite and its
mosquito vector, which determines malaria transmission
(Zewag, 2016). This indicates that, the temperature is
greater than 400C the malaria transmission is high and the
temperature is below 160C no malaria transmission
(Ezeigbo, 1998).
Fig. 6: Temperature Map
Table 5: Classification, ranking and weighting of
Temperature factor
Factor Weight Class Rank Degree of
hazard
Annual
temperature
(0c)
0.2
16-
18
1 Low Suitability
18-
20
2 Moderate
Suitability
20-
21
3 High Suitability
21-
23
4 Very High
Suitability

5. Identifying Malaria Hazard Areas Using GIS and Multi Criteria: The Case Study at East Gojjam Zone, Ethiopia
Int. J. Geogr. Reg. Plan. 136
Distance from Rivers
River is one of the various water bodies which are used for
malaria transmission. Breeding of mosquito is related with
different water sources. River is one among several of
these Mosquito requires still or slow-moving water to lay
its eggs and to complete its life cycle to be an adult
(Alemayehu, 2011). Water diverted from rivers for different
purposes and in case of over flow becomes still and favor
mosquito egg laying. This influences the particular area
with increased mosquito breeding and malaria prevalence.
Conducting irrigation practices and developing agricultural
projects around rivers can produce still water and as a
result, the changing ecosystem can cause an increase in
abundance of mosquitoes. The distance was reclassified
based on the maximum distance that mosquitoes can fly.
Fig. 7: Distance from River map
Table 6: Classification, ranking and weighting of River
factor
Factor Weight Class Rank Degree of
hazard
Distance
from
river
(meter)
0.15
0-500m 4 Very High
Suitability
500-
1500m
3 High Suitability
1500-
4500m
2 Moderate
Suitability
>4500m 1 Low Suitability
Soil Types
The study area of soil data was obtained from minster of
water and energy, Addis Ababa, Ethiopia (Abbay Basin
Soil shape file). Based on Food and Agricultural
Organization (2006), soil classification system, the study
area consists of eleven soil types. The soil layer of the
study area was classified according to their degree of hold
moisture and being permeable or impermeable.
Fig. 8: Soil map
Table 7: Classification, ranking and weighting of Soil types
factor
Factor Weight Class Rank Degree of
hazard
Soil types
based on
permeability
0.05
leptosols, eutric
fluvisols, orthic
acrisols, and
eutric regosols
1 Low
Suitability
Pellic vertisols,
chromic
cambisols,
luvisols and
vertisols, dystric
and eutric
nitisols, and
eutric cambisols
3 High
Suitability
Water body 4 Very High
Suitability
Identified Malaria Hazard Areas
In this study, some of the factors are considered to identify
malaria hazard areas. The factors are elevation, slope,
and distance from rivers, rainfall, and temperature and soil
types of malaria occurrence in the study area. It
determines number of occurrence of mosquitoes in an
area, thus these leads to overlaying of each factor to
establish and identify malaria hazard in an area. The
malaria hazard map that was prepared was derived from
several factors. The factors were ranked, according to the
degree of importance that have for the occurrence of
malaria in an area. Due to different opinions and views in
assigning rankings and weightings to each factor and their
classes based on the previous studies and experts by
using weighted linear combination method. In this study,
the final output map of malaria hazard after overlaying six
factors namely; Altitude, slope, distance from rivers,
temperature, rainfall and soil types was weighted and
ranked in ArcGIS 10.4 using weighted overlay analysis
tool, shown the following (Figure 9).

6. Identifying Malaria Hazard Areas Using GIS and Multi Criteria: The Case Study at East Gojjam Zone, Ethiopia
Addis et al. 137
Fig.9: Malaria Hazard Map
The area coverage of each suitability index of the malaria
hazard area was calculated in ArcGIS environment
showed that 840.24 Km2 (25%), 857.07 Km2 (27%), 894.60
Km2 (27%) and 716.49 Km2 (22%), representing of low,
moderate, high, and very high suitability of malaria hazard
areas respectively, with different suitability indices. Hence,
it is possible to conclude that the majority of the area (more
than 49%) is under high and very high hazard of malaria.
From the finding result, the woreda of Gozamen, Baso
liben, Awabel, and Dejen are high and very high of malaria
hazard. This is due to the lower altitude and the higher
temperatures for mosquito breeding.
CONCULUSION
This study was aimed at producing malaria hazard map of
East Gojjam zone so that it can help to improve the
management and control of malaria disease. This study
has shown that the ArcGIS is important to identify malaria
hazard areas for malaria disease control. The hazard map
was produced depending upon the factors. The malaria
incidence and transmission require the environment with
lower elevation (higher temperature), occurrence of gentle
slopes, and availability of still waters around rivers. Hence,
from the result of the overlay analysis of the factors, it is
possible to conclude that about 49% of the total area is a
highly and very high exposed to malaria hazard.
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Accepted 4 December 2019
Citation: Addis A, Assefa B, Kefyalew T (2020). Identifying
Malaria Hazard Areas Using GIS and Multi Criteria: The
Case Study at East Gojjam Zone, Ethiopia. International
Journal of Geography and Regional Planning 6(1): 132-
137.
Copyright: © 2020: Addis et al. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.